Multi-antenna techniques, such as single-user MIMO (SU-MIMO) and multi-user MIMO (MU-MIMO), are identified as key techniques for Long-Term Evolution-Advanced LTE-A to satisfy the IMT-Advanced requirements. In a MEMO system, typically various transmitted data streams are processed by proper precoding, which can increase system performance. To implement precoding, it is required that a transmitting apparatus can obtain channel state information, which, in turn, requires user equipment to feed back the channel state information to a base station. Channel information feedback accuracy has bee proven to have great impact on system performance of multi-antenna schemes, especially MU-MIMO.
In fact, there are however quite scarce resources for channel state information feedback. With a limited feedback capacity, user equipment generally first quantizes channel state information in order to increase feedback accuracy. A common approach to quantization is implemented by a codebook. In this approach, a set of codebooks (also referred to as a codebook set) is prestored at a base station and user equipment, and a receiving apparatus selects a matching precoding matrix under some criterion according to a current channel state and then feeds back a Precoding Matrix Index (PMI) to a transmitting apparatus.
One normal codebook feedback scheme uses a fixed codebook. The larger the number of feedback bits is, the larger a codebook set can be supported. This scheme is characterized by simple implementation and low feedback overheads. However, in a fixed codebook scheme, a codebook's ability to adapt to scenarios and its system performance gains are rather limited. It is impossible to have a constant codebook optimized for all antenna configurations and application scenarios.
Another codebook feedback scheme uses an adaptive codebook, wherein long-term wideband spatial correlation matrix is used to aid channel information quantization and feedback. The adaptive codebook can adaptively change according to a long-term spatial correlation matrix of each user so as to better accommodate the channel information for different users. This scheme is also termed spatial correlation based adaptive codebook feedback, which has been widely supported by many companies in 3GPP LTE-A to improve channel feedback accuracy and enhance system performance for SU/MU-MIMO.
In current adaptive codebook schemes, the baseline codebook is transformed with a long-term spatial correlation matrix, the codebook is rotated, and the transformed codebook is aligned to the preferred beamforming direction, which is the dominant eigenvector of the long-term spatial correlation matrix. The obtained adaptive codebook can be selected from only a spherical cap rather than the whole hypersphere during channel quantization. Therefore, the adaptive codebook can naturally be used to quantize rank-one channel information or dominant eigenvector information.
The spatial correlation based adaptive feedback has really shown explicit system performance gains in rank-one transmission over normal codebook feedback without spatial correlation information. But in current adaptive codebook schemes, for high rank (higher than 1) channel feedback some lower level (level 1) of eigenvectors also needs to be quantized and fed back in addition to a dominant eigenvector. Since the adaptive codebook is not aligned to the direction of the lower level of eigenvectors, the current high rank adaptive codebook is not suitable for high rank transmission. In high rank transmission, current spatial correlation based adaptive codebook feedback cannot achieve significant system performance gains and even probably lower performance than normal codebook feedback without spatial correlation information. How to further improve high rank channel feedback quality makes critical challenge for feedback mechanism design.